Real programmers don't use spreadsheets
ACM SIGPLAN Notices
Decision making using probabilistic inference methods
UAI '92 Proceedings of the eighth conference on Uncertainty in Artificial Intelligence
Extending the spreadsheet interface to handle approximate quantities and relationships
CHI '85 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Strategic Decision Making
Support vector machine active learning with applications to text classification
The Journal of Machine Learning Research
Human computation
Symbolic Evaluation and the Analysis of Programs
IEEE Transactions on Software Engineering
Get another label? improving data quality and data mining using multiple, noisy labelers
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Probabilistic Graphical Models: Principles and Techniques - Adaptive Computation and Machine Learning
Quality management on Amazon Mechanical Turk
Proceedings of the ACM SIGKDD Workshop on Human Computation
Exploring iterative and parallel human computation processes
Proceedings of the ACM SIGKDD Workshop on Human Computation
Soylent: a word processor with a crowd inside
UIST '10 Proceedings of the 23nd annual ACM symposium on User interface software and technology
Human computation: a survey and taxonomy of a growing field
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
CrowdDB: answering queries with crowdsourcing
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
Crowds in two seconds: enabling realtime crowd-powered interfaces
Proceedings of the 24th annual ACM symposium on User interface software and technology
Open source computer algebra systems: SymPy
ACM Communications in Computer Algebra
Crowdsourced enumeration queries
ICDE '13 Proceedings of the 2013 IEEE International Conference on Data Engineering (ICDE 2013)
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The wealth of resources online has empowered individuals and businesses with an unprecedented volume of information to aid in decision making processes. However, finding the many details needed for a non-trivial decision can be very labor-intensive. We present AskSheet, a general system that leverages human computation to acquire the inputs to an arbitrary decision spreadsheet provided by the user. The key innovation is the ability to prioritize the inputs by analyzing the user's spreadsheet formulas to calculate value of information for each of the blanks. By directing workers to find the details that impact the end result most, it results in a conclusive decision without gathering all of the inputs.